Image processing apparatus, image processing method and storage medium

ABSTRACT

An image processing apparatus includes: an acquisition unit configured to acquire a tomogram of an eye portion of a patient to be examined; an information acquisition unit configured to acquire information of a predetermined portion and position information of a predetermined tissue structure from the tomogram; and a calculation unit configured to calculate an evaluation value based on a relationship between the information of the predetermined portion and a position of the predetermined tissue structure.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to an image processing apparatus, an imageprocessing method, and a storage medium.

2. Description of the Related Art

Ophthalmic examinations are prevalently made for the purpose of earlierdiagnoses of various diseases that come before lifestyle-relateddiseases and causes of blindness. An ophthalmic tomography apparatussuch as an OCT (Optical Coherence Tomography) is expected to be helpfulto give more adequate diagnoses of diseases since it allows tothree-dimensionally observe the state of the interior of retina layers.In order to quantitatively measure thicknesses of layers, boundaries ofrespective retina layers are detected from a tomogram using a computer.For example, as shown in FIG. 10B, an inner limiting membrane B1, innerplexiform layer boundary B2, boundary B3 between photoreceptor inner andouter segments, and retinal pigment epithelium boundary B4 are detected,and retina thicknesses T1 and GCC (Ganglion Cell Complex) thicknesses T2are measured.

Tomograms of an eye portion captured using the OCT include regions(cells, tissues, portions) which influence a visual function, and adoctor observes conditions of damages of corresponding cells, layerthicknesses, and positional relationships with a portion such as a foveacentralis F1 (FIG. 10A) upon predicting the restoration possibility andprognosis of the visual function. For example, FIG. 10B shows anenlarged view of a region Ro in FIG. 10A. Light which has reached aretina is converted into an electrical signal by an outer segment L1 ofa photoreceptor cell C1, and is perceived by a visual cortex (not shown)of the cerebrum via a bipolar cell C2, a ganglion cell C3 and opticnerve (not shown) in turn. If the photoreceptor outer segment L1 isdamaged, since it can no longer convert light into an electrical signal,the visual function lowers at the damaged portion. The photoreceptorcell C1 includes a petrosa and rod, and the petrosa controls the visualfunction in a bright place. As shown in FIG. 10C, since the petrosa ispresent at a higher density as it is closer to the fovea centralis F1,the influence of the photoreceptor outer segment damage on the visualfunction per unit area is greater as it is closer to the fovea centralisF1. Note that a visual axis is a line which connects an object to begazed and the fovea centralis F1. Therefore, in order to estimate theinfluence of the photoreceptor outer segment damage on the visualfunction, a degree of damage (length of the outer segment) at theposition of the photoreceptor outer segment damage, an existence range(area) of the damage region, and a distance of the damage region fromthe fovea centralis have to be taken into consideration.

Furthermore, since the photoreceptor cell C1 derives nutrition fromchoroidal vessels V1, as shown in FIG. 10D, if a lesion such as anamotio retinae RD exists between the photoreceptor outer segment L1 anda retinal pigment epithelium L2, nutrition cannot be supplied to thephotoreceptor cell C1. Therefore, if the amotio retinae RD exists, thevisual function is more likely to lower in the future. If existence ofthe amotio retinae RD is protracted, the photoreceptor cell C1 becomesextinct, and the visual function can no longer be restored. Therefore,in order to estimate the influence on the visual function or to predictthe prognosis of the visual function from the retina shape, not only thethicknesses of cells and layers are simply measured, but also thepositional relationships with a portion such as the fovea centralis F1,and the presence/absence or existence period of a lesion such as theamotio retinae RD have to be taken into consideration. For the purposeof diagnosis support of a glaucoma and optic nerve disease, a techniqueof measuring the GCC thicknesses T2 related to a visual field as one ofthe visual functions, and displaying differences from GCC thicknesses T2in a healthy eye as a map is disclosed in WO/2008/157406 (to be referredto as literature 1 hereinafter).

However, the technique described in literature 1 assumes an applicationto a glaucoma, and does not consider any positional relationship withthe fovea centralis F1 and any influence of an exudative lesion on thevisual function, which are to be considered in case of a maculardisease. Also, the technique described in literature 1 presents only oneor more layer thickness maps (parallelly) for predictions of therestoration possibility and prognosis of the visual function, and therestoration possibility and prognosis of the visual function have to bevisually judged from the maps.

Also, a technique described in Japanese Patent Laid-Open No. 2009-34480is premised on the presence of test results of a perimeter, and measureslayer thicknesses of a portion, the anomaly of which was revealed by thevisual field test to examine correspondence with visual functionevaluation values by the visual field test. As is known, a change inretina shape appears prior to a change in visual function, and it isdesirable to predict the prognosis based only on information obtainedfrom ophthalmic tomograms in terms of early detection of a retinaldisease.

SUMMARY OF THE INVENTION

The present invention provides an image processing technique whichdetermines an influence degree on a visual function using only anophthalmic tomogram by quantitating positional relationships with apredetermined portion and a distribution condition of lesion locations,and can provide this determination result as diagnosis supportinformation.

According to one aspect of the present invention, there is provided animage processing apparatus comprising: an acquisition unit configured toacquire a tomogram of an eye portion of a patient to be examined; aninformation acquisition unit configured to acquire information of apredetermined portion and position information of a predetermined tissuestructure from the tomogram; and a calculation unit configured tocalculate an evaluation value based on a relationship between theinformation of the predetermined portion and a position of thepredetermined tissue structure.

According to the arrangement of the present invention, an influencedegree on a visual function is determined using only an ophthalmictomogram by quantitating positional relationships with a predeterminedportion and a distribution condition of lesion locations, and thisdetermination result can be provided as diagnosis support information.

Further features of the present invention will become apparent from thefollowing description of exemplary embodiments (with reference to theattached drawings).

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing an example of the functionalarrangement of an image processing apparatus according to the firstembodiment;

FIG. 2 is a flowchart showing the sequence of processing to be executedby the image processing apparatus according to the first embodiment;

FIGS. 3A to 3C are views for explaining the image processing contents inthe first embodiment;

FIG. 4 is a flowchart showing details of processing executed in stepS240 according to the first embodiment;

FIG. 5 is a flowchart showing details of processing executed in stepS250 according to the first embodiment;

FIG. 6 is a view for explaining the image processing contents in thesecond embodiment;

FIGS. 7A and 7B are views for explaining the image processing contentsin the third embodiment;

FIG. 8 is a flowchart showing details of processing executed in stepS240 according to the third embodiment;

FIG. 9 is a flowchart showing details of processing executed in stepS250 according to the third embodiment;

FIGS. 10A to 10D are views associated with a tomogram of a macularregion of a retina captured by an OCT;

FIG. 11 is a diagram showing an example of the configuration of a systemincluding the image processing apparatus according to the embodiment;and

FIG. 12 is a block diagram showing an example of the hardwarearrangement of the image processing apparatus.

DESCRIPTION OF THE EMBODIMENTS First Embodiment

An image processing apparatus according to this embodiment is configuredto predict the influence on a visual function and the prognosis of thevisual function based on the positional relationship between a thinnedregion of photoreceptor outer segment thicknesses and a fovea centralisF1, the presence/absence of an amotio retinae RD, and the like. An imageprocessing apparatus according to embodiments of the present inventionand an image processing method in the image processing apparatus will bedescribed hereinafter with reference to the accompanying drawings.

FIG. 12 is a block diagram showing an example of the hardwarearrangement of an image processing apparatus 10. A CPU 1201 controls theoverall operations of the image processing apparatus 10. A ROM 1203 andexternal storage device 1204 store programs which are executable by theimage processing apparatus 10, and parameters. A RAM 1202 serves as awork area used when programs are executed. A monitor 1205 serves as anoutput unit, and displays image processing results and calculationprocessing results. A keyboard 1206 and mouse 1207 serve as an inputunit. The user can input various commands to the image processingapparatus via the keyboard 1206 and mouse 1207. An interface 1208connects the image processing apparatus 10 to a network (LAN) 30. Therespective components shown in FIG. 12 are connected via a bus 1209.

FIG. 11 is a diagram showing the configuration of apparatuses which areconnectable to the image processing apparatus 10. A tomography apparatus20 is connectable to a data server 40 via the network (LAN) 30 such asEthernet®. The image processing apparatus 10 is connectable to thetomography apparatus 20 via the network (LAN) 30 or an interface such asan optical fiber, USB, or IEEE1394. The tomography apparatus 20 is anapparatus for capturing ophthalmic tomograms, and is, for example, atime-domain or Fourier-domain OCT. The tomography apparatus 20three-dimensionally captures tomograms of an eye to be examined (notshown) in response to an operation by an operator (not shown). Thecaptured tomograms are transmitted to the image processing apparatus 10.The data server 40 is a server, which holds the tomograms, image featureamounts, and the like of the eye to be examined, and saves the tomogramsof the eye to be examined output from the tomography apparatus 20 andanalysis results output from the image processing apparatus 10. Also, inresponse to a request from the image processing apparatus 10, the dataserver 40 transmits previous data associated with the eye to be examinedto the image processing apparatus 10.

Note that this embodiment will explain a case in which the imageprocessing apparatus 10 measures a boundary B3 between photoreceptorinner and outer segments, and retinal pigment epithelium boundary B4.However, the present invention is not limited to this. The imageprocessing apparatus 10 can detect other layer boundaries (an externallimiting membrane (not shown), an inner boundary of a stratum pigmentibulbi (not shown), and the like.

FIG. 1 is a block diagram showing an example of the functionalarrangement of the image processing apparatus 10. The image processingapparatus 10 includes a tomogram acquisition unit 110, storage unit 120,image processing unit 130, diagnosis support information output unit140, and instruction acquisition unit 150. The image processing unit 130includes an ophthalmic feature acquisition unit 131, lesion detectionunit 132, and visual function influence degree determination unit 133.The visual function influence degree determination unit 133 includes avisual function restoration possibility determination unit 1331 andvisual function prognosis prediction unit 1332. The diagnosis supportinformation output unit 140 includes a visual function influence degreeinformation output unit 141, visual function restoration possibilityinformation output unit 142, and visual function prognosis predictioninformation output unit 143.

The functions of respective blocks included in the image processingapparatus 10 will be described below in association with a practicalexecution sequence of the image processing method in the imageprocessing apparatus 10 shown in the flowchart of FIG. 2. In step S210,the tomogram acquisition unit 110 transmits a tomogram acquisitionrequest to the tomography apparatus 20. The tomography apparatus 20transmits corresponding tomograms in response to the acquisitionrequest. The tomogram acquisition unit 110 receives the tomogramscorresponding to the acquisition request from the tomography apparatus20 via the network 30. The tomogram acquisition unit 110 stores thereceived tomograms in the storage unit 120.

In step S220, the ophthalmic feature acquisition unit 131 acquiresinformation of predetermined portions and position information ofpredetermined tissue structures from tomograms. The ophthalmic featureacquisition unit 131 acquires ophthalmic features (anatomic features),which represent predetermined cells, issues, or portions, from thetomograms. The ophthalmic feature acquisition unit 131 can detect, asophthalmic features, for example, an inner limiting membrane B1,boundary B3 between photoreceptor inner and outer segments, and retinalpigment epithelium boundary B4 from the tomograms stored in the storageunit 120. The ophthalmic feature acquisition unit 131 then stores thedetected features in the storage unit 120.

A practical ophthalmic feature acquisition sequence will be explainedbelow. A processing method for detection of boundaries of respectiveretina layers will be explained first. In this case, a volume image tobe processed is considered as a set of two-dimensional tomograms (B-scanimages), and the following processing is applied to respectivetwo-dimensional tomograms. A two-dimensional tomogram of interestundergoes smoothing processing to remove noise components. Next, edgecomponents are detected from the two-dimensional tomogram, and some linesegments are extracted as candidates of layer boundaries based on theirconnectivity. Then, from the extracted candidates of layer boundaries,the uppermost line segment is selected as the inner limiting membraneB1. Also, the line segment, which is located on the outer layer side(the larger z-coordinate side in FIG. 10A) of the inner limitingmembrane B1 and has a maximum contrast, is selected as the boundary B3between photoreceptor inner and outer segments. Furthermore, thelowermost line segment of the extracted candidates is selected as theretinal pigment epithelium boundary B4.

Furthermore, layer boundaries may be precisely extracted by applying adeformable model such as Snakes or a level-set method to have these linesegments as initial values. Also, layer boundaries may be detected by agraph cut method. Note that boundary detection using the deformablemodel or graph cut method may be three-dimensionally executed for avolume image, or may be two-dimensionally executed for respectivetwo-dimensional tomograms. Also, arbitrary layer boundary detectionmethods may be used as long as they can detect layer boundaries from anophthalmic tomogram.

In step S230, the lesion detection unit 132 detects the presence/absenceof lesion candidates (amotio retinae) within a region defined by theophthalmic features acquired in step S220, for example, within a regionsandwiched between the boundary B3 between photoreceptor inner and outersegments and the retinal pigment epithelium boundary B4. Note that thisembodiment will describe a case in which the amotio retinae RD existsbelow a fovea centralis. The amotio retinae RD is likely to develop atevery location in an eye fundus other than a Mariotte blind spot, andthe image processing method of this embodiment is applicable to a casein which the amotio retinae RD has developed in an arbitrary location(except for the Mariotte blind spot) within a capturing range of thetomograms.

A detection method of the amotio retinae RD will be described below.Lesion candidate regions are detected by comparing luminance levels inthe region defined by the ophthalmic features (anatomic features) with apredetermined luminance threshold Trd. For example, regions which assumelower luminance values than the threshold are detected as lesioncandidates. As shown in FIGS. 3A and 3C, low-luminance regions, whichare located within a region, which is located on the outer layer side(the larger z-coordinate side) of the boundary B3 between photoreceptorinner and outer segments and on the inner layer side (the smallerz-coordinate side) of the retinal pigment epithelium boundary B4 onrespective A-scan lines in the x-axis direction on an x-z plane, areextracted as lesion candidate regions. As the extraction method, a knownextraction method is applicable. In this case, regions having lowerluminance values than the luminance threshold Trd within theaforementioned region are detected as amotio retinae candidate regions(lesion candidate regions).

The lesion detection unit 132 quantitates sizes of portions responsiblefor the visual function based on the ophthalmic features (the foveacentralis F1, inner limiting membrane B1, boundary B3 betweenphotoreceptor inner and outer segments, and retinal pigment epitheliumboundary B4) and the positions of the lesion candidates (amotio retinaeRD). The lesion detection unit 132 measures (quantitates) thicknesses(photoreceptor outer segment thicknesses) Tp(x, y) of a photoreceptorouter segment L1 at respective coordinate points within an x-y plane, asa relationship between the positions and sizes. The photoreceptor outersegment L1 responsible for the visual function converts light, which hasreached the retina of an eye portion of a patient to be examined, intoan electrical signal, so as to control the visual function. In thiscase, as a simple measurement method of Tp(x, y), values obtained bysubtracting distances in the z-axis direction of the amotio retinaeregion from a distance in the z-axis direction between the boundary B3between photoreceptor inner and outer segments and the retinal pigmentepithelium boundary B4 on respective A-scan lines in the x-axisdirection on the x-z plane are used. Note that the measurement method ofthe thicknesses of the photoreceptor outer segment L1 is not limited tothis. For example, the inner boundary of the stratum pigmenti bulbi (notshown) may be extracted in step S220, and values obtained by subtractingthe amotio retinae thicknesses from the distance between the boundary B3between photoreceptor inner and outer segments and the inner boundary ofthe stratum pigmenti bulbi (not shown) may be used as Tp(x, y).

Next, the lesion detection unit 132 acquires a normal value Tn of thephotoreceptor outer segment thickness, and normal value data (see FIG.3B) associated with a density distribution ω(a) [the number ofcells/mm²] of photoreceptor cells within the x-y plane from the dataserver 40. In this case, the lesion detection unit 132 acquires dataassociated with the petrosal of the photoreceptor cells. An abscissa isa visual angle [°] from a visual axis (a line which connects acrystalline lens and the fovea centralis).

In step S240, the visual function influence degree determination unit133 calculates evaluation values from the relationship between theinformation of the predetermined portions and the positions of thepredetermined issue structures. The visual function influence degreedetermination unit 133 calculates influence degrees on the visualfunction using the thicknesses (photoreceptor outer segment thicknesses)Tp(x, y) of the photoreceptor outer segment L1 quantitated inassociation with the portions responsible for the visual function. Inthis step, determination of a restoration possibility of the visualfunction, and that of a possibility of a visual function drop (prognosisprediction) can also be made together. Details of this step will beexplained later with reference to FIG. 4.

In step S250, the diagnosis support information output unit 140 outputsthe determination result of the visual function influence degreedetermination unit 133 as diagnosis support information. The diagnosissupport information output unit 140 generates a two-dimensionalcoordinate map (visual function influence degree map) which has valuesof the influence degrees calculated in the determination step S240 aspixel values of the tomogram, and outputs that map as the diagnosissupport information. Also, the diagnosis support information output unit140 can generate a two-dimensional coordinate map (visual functionrestoration possibility map) which has indices (values) indicatingrestoration possibilities of the visual function as pixel values of thetomogram, and can output that map as the diagnosis support information.Furthermore, the diagnosis support information output unit 140 cangenerate a two-dimensional coordinate map (visual function drop riskdegree map) having indices (values) indicating visual function droppossibilities as pixel values of the tomogram, and can output that mapas the diagnosis support information. In this case, the visual functioninfluence degree map, visual function restoration possibility map, andvisual function drop risk degree map are included in the diagnosissupport information, and determination results of the visual functioninfluence degree determination unit 133 are visualized by displaycontrol for displaying those determination results on thetwo-dimensional coordinate maps.

In step S260, the instruction acquisition unit 150 externally acquiresan instruction as to whether or not to save the current processingresults associated with the eye to be examined in the data server 40.This instruction is input by the operator via, for example, the keyboard1206 and mouse 1207 shown in FIG. 12. If the processing result saveinstruction is issued, the process advances to step S270; otherwise, theprocess jumps to step S280.

In step S270, the diagnosis support information output unit 140transmits a date and time of inspection, identification informationrequired to identify the eye to be examined, the tomograms, the analysisresults of the image processing unit 130, and the diagnosis supportinformation obtained by the diagnosis support information output unit140 to the data server 40 in association with each other.

In step S280, the instruction acquisition unit 150 externally acquiresan instruction as to whether or not to end the tomogram analysisprocessing by the image processing apparatus 10. This instruction isinput by the operator via the keyboard 1206 and mouse 1207 shown in FIG.12. If the processing end instruction is acquired, the analysisprocessing ends. On the other hand, if a processing continue instructionis acquired, the process returns to step S210 to execute processing forthe next eye to be examined (or re-processing for the same eye to beexamined).

(Visual Function Influence Degree Determination Processing)

Details of the processing executed in step S240 will be described belowwith reference to FIG. 4. In step S410, the visual function influencedegree determination unit 133 calculates ratios (Tn/Tp) of the normalvalue data associated with the size of a portion responsible for thevisual function, which size is quantitated by the lesion detection unit132, to the sizes Tp(x, y) of that portion. Then, the visual functioninfluence degree determination unit 133 calculates a value indicating aninfluence degree by multiplying the normal value data (ω(a)) indicatingthe density distribution of the photoreceptor cells by the ratios. Thevisual function influence degree determination unit 133 can acquire thenormal value data (Tn) associated with the size of the portionresponsible for the visual function, and the data (ω(a)) indicating thedensity distribution of the photoreceptor cells from, for example, thedata server 40. More specifically, the visual function influence degreedetermination unit 133 calculates an influence degree S₁ on the visualfunction by equation (1) below. The calculation of the value indicatingthe influence degree on the visual function uses the photoreceptor outersegment thicknesses Tp(x, y) at respective points on the x-y planedetected in step S230, the normal value Tn associated with the positionand shape (for example, the thickness) of the photoreceptor outersegment, and the normal value data associated with the densitydistribution ω(a) of the photoreceptor cells within the x-y plane. Inthis case, a visual acuity will be examined as the visual function.

S ₁=Σ{ω(a)·Tn/Tp(x,y)}  (1)

where Tp(x, y)/Tn represents a degree of thinning of a petrosal outersegment, and Tn=60 μm in this embodiment. As can be understood fromequation (1), the influence degree on the visual function is larger as athinned region of the petrosal outer segment is broader, and as adistance between the thinned region and fovea centralis is smaller.

In step S420, the visual function restoration possibility determinationunit 1331 determines a visual function restoration possibility R₁ atrespective points (x, y) on the x-y plane from the ratios between thephotoreceptor outer segment thicknesses obtained in step S230 and thenormal value of the photoreceptor outer segment thickness, as given by:

$\begin{matrix}{R_{1} = {\sum\left\{ A \right\}}} & (2) \\{A = \left\{ \begin{matrix}{\left( \frac{{Tp}\left( {x,y} \right)}{Tn} \right) \cdot \left( \frac{1}{{RD}(t)} \right)} & \left( {\frac{{Tp}\left( {x,y} \right)}{Tn} \geq {Tr}} \right) \\0 & \left( {\frac{{Tp}\left( {x,y} \right)}{Tn} < {Tr}} \right)\end{matrix} \right.} & \left( 2^{\prime} \right)\end{matrix}$

where RD(t) is an existence period of the amotio retinae.

If the ratio Tp/Tn is less than a threshold associated with the ratio(<Tr), the visual function restoration possibility determination unit1331 determines that it is difficult to restore the photoreceptor outersegment, and calculates an index (value) indicating the restorationpossibility of the visual function at that position (x, y) as zero. IfTp/Tn is equal to or larger than the threshold associated with the ratio(≧Tr), the visual function restoration possibility is calculated byequation (2′). Also, since it is considered that the photoreceptor cellshave poorer nutrition as the existence period of the lesion candidate(the existence period RD(t) of the amotio retinae RD) is longer, R₁assumes a smaller value since it is multiplied by (1/the existenceperiod (RD(t)) of the lesion candidate). Note that when information ofthe existence period RD(t) of the amotio retinae is not available fromthe data server 40, R₁ is calculated while RD(t)=1 in equation (2′).

In step S430, the visual function prognosis prediction unit 1332calculates a visual function drop risk degree P₁ as an index (value)associated with prognosis prediction of the visual function. This degreeis an index (value) indicating a visual function drop possibility in thefuture. Note that RDe(x, y) is a value, which is set according to thepresence/absence of the lesion candidate (amotio retinae) at each point(x, y) on the x-y plane. For example, the lesion detection unit 132 setsRDe(x, y)=1 if an amotio retinae is detected at position coordinates (x,y), and sets RDe(x, y)=0 if no amotio retinae is detected. The visualfunction prognosis prediction unit 1332 acquires the normal value data(ω) indicating the density distribution of the photoreceptor cells andthe setting data (RDe) set according to the presence/absence of thelesion candidates, and makes a calculation given by:

P ₁=Σ{ω(a)·RDe(x,y)}  (3)

If the existence period RD(t) of the amotio retinae at each point (x, y)on the x-y plane is available, an estimated value P₁′ of a visualfunction drop time can be generated by:

P ₁′=Σ{ω(a)·(Td−RD(t))}  (3′)

where Td is data indicating a maximum survival period of thephotoreceptor outer segment without any nutrient supply from a choroidcoat, and can be acquired from the data server 40 or storage unit 120.

(Diagnosis Support Information Output Processing)

Details of the processing executed in step S250 will be described belowwith reference to FIG. 5.

In step S510, the diagnosis support information output unit 140 acquiresdata associated with the visual function influence degrees, visualfunction restoration possibilities, and visual function drop riskdegrees from the image processing unit 130. Then, the diagnosis supportinformation output unit 140 transmits the respective pieces ofinformation to the visual function influence degree information outputunit 141, visual function restoration possibility information outputunit 142, and visual function prognosis prediction information outputunit 143.

In step S520, the visual function influence degree information outputunit 141 generates a map having, as values, influence degrees on thevisual function at respective points on the x-y plane, which arecalculated in step S410, that is, ω(a)·Tn/Tp(x, y). This map visualizesthe influence degrees on the visual function, and represents the visualfunction influence degrees at respective points (x, y) at the imagecapturing timing. On the monitor 1205 (FIG. 12), not only the visualfunction influence degree map but also the influence degree S₁calculated in step S410 is output at the same time.

In step S530, the visual function restoration possibility informationoutput unit 142 generates a map having values ((Tp(x, y)/Tn)·(1/RD(t))or 0) of the visual function restoration possibilities at respectivepoints on the x-y plane, which are calculated in step S420. Also, thevisual function prognosis prediction information output unit 143generates a map having values (ω(a)·RDe(x, y)) of the visual functiondrop risk degrees, which are calculated in step S420.

In this case, when the existence periods RD(t) of the amotio retinae atrespective points (x, y) on the x-y plane are available, a map having,as values, estimated values of the visual function drop times at therespective points (x, y), that is, ω(a)·(Td−RD(t)), is generated. Notethat Td represents a maximum survival period of the photoreceptor outersegment without any nutrient supply from a choroid coat.

On the monitor 1205, in addition to the aforementioned maps, the indexR₁ associated with the visual function restoration possibility, theindex P₁ associated with the visual function drop risk degree, and theindex P₁′ associated with the visual function drop time estimated valuecan also be output.

According to this embodiment, the image processing apparatus calculatesvalues indicating the influence degrees on the visual function accordingto the positional relationship between the thinned region of thephotoreceptor outer segment thicknesses and the fovea centralis. Also,the image processing apparatus checks the presence/absence of an amotioretinae, and also calculates the visual function drop risk degrees usinginformation indicating the photoreceptor outer segment thicknesses andthe presence/absence of the amotio retinae. Thus, the image processingapparatus can predict the influence of the lesion candidates on thevisual function and the prognosis of the visual function from thetomogram of a macular disease.

Second Embodiment

This embodiment will examine a case in which exudative lesions such asleukomas EX are collected under a fovea centralis F1 as a case of theinfluence imposed on the visual function in place of layer shapeanomalies unlike in the first embodiment. The leukomas EX are extracted,and the influence on the visual function and the prognosis of the visualfunction are predicted based on the positional relationship between adistribution of the leukomas EX and ophthalmic features such as thefovea centralis F1.

The configuration of apparatuses connectable to the image processingapparatus 10 according to this embodiment and the functional arrangementof the image processing apparatus 10 are the same as those in the firstembodiment. Also, the image processing sequence of this embodiment isthe same as that (FIG. 2) in the first embodiment, except for processesin steps S230, S240, and S250 in FIG. 2. Hence, only the processes insteps S230, S240, and S250 will be described below.

As shown in FIG. 6, massive high-luminance regions called leukomas EXare formed in a retina that suffers a retinopathy of diabetes, since fatand protein leaking from retinal blood vessels are collected. Theleukomas EX are normally formed in the vicinity of an outer plexiformlayer. Since locations of the leukomas EX shield incoming light andlight cannot reach photoreceptor cells C1, a visual acuity lowers.Especially, as the locations of the leukomas EX are closer to the foveacentralis F1, the influence on the visual function is serious due to ahigh petrosal density.

If an amotio retinae RD exists below the fovea centralis F1, it isempirically known that leukomas EX tend to be collected toward theamotio retinae RD. Once the leukomas EX are collected below the foveacentralis F1, a visual acuity considerably lowers, and recovery of thevisual acuity cannot be expected. Therefore, it is helpful to knowdistances between the leukomas EX and subretina and whether or not theleukomas EX move toward the subretina for the purpose of prevention of avisual function drop due to collection of lesions below the foveacentralis.

(Processing in Step S230)

In the processing in step S230 in the second embodiment, the lesiondetection unit 132 detects leukomas EX in an ophthalmic tomogram aslesion candidates. In this case, the leukomas EX are identified asfollows by combining information of luminance values and output valuesof a filter such as a point convergence filter, which emphasizes massivestructures. That is, a region where an output of the point convergencefilter is equal to or larger than a threshold Ta and a luminance valueon a tomogram is equal to or larger than a threshold Tb is identified asa leukoma EX. Note that the detection method of the leukomas EX is notlimited to this, and an arbitrary known lesion detection method can beused. Also, as in the first embodiment, the presence/absence of anamotio retinae RD is detected within a region sandwiched between theboundary B3 between photoreceptor inner and outer segments and theretinal pigment epithelium boundary B4 detected in step S220.

Next, the lesion detection unit 132 quantitates a distribution of theleukomas EX in the retina based on the detected lesions (leukomas EX andamotio retinae RD). More specifically, the lesion detection unit 132labels the detected leukomas EX. As a result of labeling, label valuesare respectively set for the leukomas EX. In this case, a regionexpansion method is used as the labeling method. Note that the labelingmethod is not limited to this, and an arbitrary known labeling methodcan be used. Next, a volume of the leukomas EX having the same labelvalue is calculated. When the amotio retinae RD exists in the retina, ashortest distance do of a leukoma EX of a label n to the amotio retinaeRD is calculated.

(Processing in Step S240)

Details of the processing to be executed in step S240 of the secondembodiment will be described below with reference to FIG. 6 and theflowchart shown in FIG. 4.

In step S410, the visual function influence degree determination unit133 calculates a value S₂ indicating an influence degree on the visualfunction using equation (4) below. In this case, a visual acuity will beexamined as the visual function.

S ₂ =ΣnΣarea{ω(a)}  (4)

where ω(a) is a petrosal density [the number of cells/mm²] which is thesame as that in the first embodiment, area represents an area whenleukomas EX having the same label are projected onto the x-y plane, andn represents the number of leukomas EX which exist within an imagecapturing region.

In step S420, the visual function restoration function possibilitydetermination unit 1331 calculates an index R₂ associated with a visualfunction restoration possibility using:

R ₂ =Σn{ω(a)·d _(n)}  (5)

where n is the total number of detected leukomas EX, v_(n) is a volumeof leukomas EX of a label n, and d_(n) is a distance between thebarycentric point of the leukomas EX of the label n and the amotioretinae RD.

In step S430, the visual function prognosis prediction unit 1332calculates a visual function drop risk degree, that is, a lesioncollection risk degree P₂ under the fovea centralis F1 using:

P ₂ =Σn{v _(n)/(d _(n)+1)}  (6)

(Processing in Step S250)

Details of the processing to be executed in step S250 will be describedbelow with reference to FIG. 5. Note that since step S510 is the same asthat in the first embodiment, a description thereof will not berepeated.

In step S520, the visual function influence degree information outputunit 141 generates a map (visual function influence degree map) havingvalues indicating influence degrees on the visual function at respectivepoints (x, y) on the x-y plane, which are calculated in step S410. Onthe monitor 1205 shown in FIG. 12, not only the visual functioninfluence degree map, but also the value S₂ indicating the visualfunction influence degree calculated in step S410 can be output at thesame time.

In step S530, the visual function restoration possibility informationoutput unit 142 and visual function prognosis prediction informationoutput unit 143 generate a map having values indicating visual functionrestoration possibilities at respective points on the x-y plane, whichare calculated in step S420, and a map having values indicating visualfunction drop risk degrees, which are calculated in step S430.Furthermore, on the monitor 1205 shown in FIG. 12, not only the visualfunction restoration possibility map and visual function drop riskdegree map, but also the index R₂ associated with the visual functionrestoration possibility and the visual function drop risk degree P₂ canbe output at the same time.

With the aforementioned arrangement, the image processing apparatus 10extracts the leukomas EX and amotio retinae RD as lesions, and canpredict the influence on the visual function and the prognosis of thevisual function based on the positional relationships with theophthalmic features such as the fovea centralis F1.

Third Embodiment

This embodiment will examine a case in which the influence on the visualfunction appears due to a shape anomaly of a tissue called a cribrosalamina L3, which exists in a deep layer of an optic papilla unlike inthe first embodiment. Extraction and shape measurement of layerboundaries and the cribrosa lamina L3 in a retina are made, and theinfluence on the visual function and the prognosis of the visualfunction are predicted using information of positional relationshipswith ophthalmic features such as a Mariotte blind spot in addition to afovea centralis F1.

As shown in FIGS. 7A and 7B, a cribrum-shaped film called a cribrosalamina L3 exists below the optic papilla. The cribrosa lamina L3 has adisc shape when viewed from a direction perpendicular to the x-y plane,and about 600 to 700 pores are formed. Optic nerve fiber bundles, thatis, sets of axons of photoreceptor cells C1 pass through these pores,and are extended toward a brain. When an intraocular pressure rises, thecribrosa lamina L3 is deflected to dislocate pore positions, and topress the axons, resulting in extinction of the photoreceptor cells C1.

The configuration of apparatuses connectable to the image processingapparatus 10 according to this embodiment is the same as that of thefirst embodiment. Also, the functional arrangement of the imageprocessing apparatus 10 is the same as that of the first embodiment,except that the visual function influence degree determination unit 133does not include the visual function restoration possibilitydetermination unit 1331, and the diagnosis support information outputunit 140 does not include the visual function restoration possibilityinformation output unit 142. This reflects that once the photoreceptorcells C1 have become extinct to impose the influence on the visualfunction, the visual function can no longer be restored. The imageprocessing sequence of this embodiment is the same as that (FIG. 2) ofthe first embodiment, except for the processes in steps S220, S230,S240, and S250 shown in FIG. 2. Hence, only the processes in steps S220,S230, S240, and S250 will be explained below.

(Processing in Step S220)

In the processing in step S220 in the third embodiment, the ophthalmicfeature acquisition unit 131 detects an optic papilla and foveacentralis F1 by detecting a fovea from a tomogram. Furthermore, theophthalmic feature acquisition unit 131 extracts retinal blood vesselsin a projected image of the tomogram, and determines a portion includingthe retinal blood vessels as the optic papilla, and a portion withoutincluding any retinal blood vessels as the fovea centralis F1. Theretinal blood vessel extraction method uses an arbitrary known lineemphasis filter. The optic papilla fovea is unperceivable and is calleda Mariotte blind spot since it does not include any photoreceptor cellsC1.

Next, the ophthalmic feature acquisition unit 131 acquires an innerlimiting membrane B1, nerve fiber layer boundary B5, and retinal pigmentepithelium boundary B4 as layer boundaries. The layer boundaryextraction method is the same as that in the first embodiment, and adescription thereof will not be repeated. Furthermore, the ophthalmicfeature acquisition unit 131 detects the cribrosa lamina L3 in thefollowing sequence. That is, at respective points (x, y) in a region(Mariotte blind spot) where no retinal pigment epithelium boundary B4exists on the x-y plane, a high-luminance region which is located on thedeep layer side of the inner limiting membrane B1 (on the positivedirection side of the z axis) and in which luminance values are equal toor higher than a certain threshold Tx is detected. The cribrosa laminaL3 is detected as a porous disc-shaped region on the x-y plane. In thisembodiment, the nerve fiber layer boundary B5 is used. Instead, an innerplexiform layer boundary B2 may be acquired. However, in this case, GCC(Ganglion Cell Complex) thicknesses are measured in step S230 in placeof the nerve fiber layer thicknesses.

(Processing in Step S230)

In the processing in step S230 in the third embodiment, the lesiondetection unit 132 measures nerve fiber layer thicknesses using theinner limiting membrane B1 and nerve fiber layer boundary B5 acquired instep S220. Next, the lesion detection unit 132 acquires a normal valuerange of the nerve fiber layer thicknesses from the data server 40, anddetects points (x′, y′) outside the Mariotte blind spot, which have thenerve fiber layer thicknesses falling outside the normal value range, aslesion locations. Furthermore, the lesion detection unit 132 measurescribrosa lamina thicknesses and roughened patterns of a cribrosa laminaboundary, and detects points (x, y) inside the Mariotte blind spot,which fall outside normal value ranges of the cribrosa laminathicknesses and roughened patterns that are acquired from the dataserver 40, as lesion locations.

(Processing in Step S240)

Details of the processing executed in step S240 will be described belowwith reference to FIG. 8. In step S810, the visual function influencedegree determination unit 133 calculates a value S₃ indicating a visualfunction influence degree from the nerve fiber layer thicknessesacquired in step S230 according to:

$\begin{matrix}{{{S_{3} = {\sum x}},{y\left\{ B \right\}}}{B = \left\{ \begin{matrix}{{\alpha \left( {x,y} \right)} \cdot {T_{n{({x,y})}}/T_{1{({x,y})}}}} & \left( {{outside}\mspace{14mu} {forvea}\mspace{14mu} {contralis}} \right) \\1 & \left( {{fovea}\mspace{14mu} {centralis}} \right)\end{matrix} \right.}} & (7)\end{matrix}$

where T_(n(x,y)) is a normal value (median thereof) of the nerve fiberlayer thickness at the point (x, y) on the x-y plane. T_(l(x,y)) is thenerve fiber layer thickness at the point (x, y) on the x-y plane, whichis determined as a nerve fiber layer thickness anomaly in step S230.α(x, y) is a variable which assumes 0 at a central Mariotte blind spot,or 1 at other spots.

In step S820, the visual function prognosis prediction unit 1332calculates a visual function drop risk degree P₃ from the thicknessesand roughened patterns of the cribrosa lamina L3 acquired in step S230according to:

P ₃ =Σx,y(T _(ns) /T _(s))+k·ΣyΣx{Π/θ _(x,y)}  (8)

where θ_(x,y) is an angle [rad] three neighboring points (three pointsx−1, x, and x+1 in the x-axis direction, and three points y−1, y, andy+1 in the y-axis direction) of control points that configure theboundary of the cribrosa lamina make. Also, k is a proportionalconstant.

(Processing in Step S250)

Details of the processing executed in step S250 will be described belowwith reference to FIG. 9.

In step S910, the diagnosis support information output unit 140 acquiresdata associated with the visual function influence degrees and visualfunction drop risk degrees from the image processing unit 130, andtransmits them to the visual function influence degree informationoutput unit 141 and visual function prognosis prediction informationoutput unit 143, respectively.

In step S920, the visual function influence degree information outputunit 141 generates a map (visual function influence degree map) havingvalues indicating influence degrees on the visual function at respectivepoints (x, y) on the x-y plane, which are calculated in step S810. Onthe monitor 1205 shown in FIG. 12, not only the visual functioninfluence degree map but also the index (value) of the visual functioninfluence degree calculated in step S810 can also be output at the sametime.

In step S930, the visual function prognosis prediction informationoutput unit 143 generates a map having values indicating visual functiondrop risk degrees at respective points on the x-y plane, which arecalculated in step S820. Furthermore, on the monitor 1205 shown in FIG.12, not only the visual function drop risk degree map but also thevisual function drop risk degree calculated in step S820 can also beoutput at the same time.

According to the aforementioned arrangement, the image processingapparatus 10 extracts a shape anomaly (thinned or roughened) region ofthe cribrosa lamina in the optic papilla, and calculates visual functioninfluence degrees and visual function drop risk degrees in accordancewith the positional relationships with the fovea centralis and Mariotteblind spot. Thus, in a case of an early glaucoma, the influence on thevisual function and the prognosis of the visual function can bepredicted based on the distribution of lesions below the retina from anophthalmic tomogram of the optic papilla.

Other Embodiments

Aspects of the present invention can also be realized by a computer of asystem or apparatus (or devices such as a CPU or MPU) that reads out andexecutes a program recorded on a memory device to perform the functionsof the above-described embodiment(s), and by a method, the steps ofwhich are performed by a computer of a system or apparatus by, forexample, reading out and executing a program recorded on a memory deviceto perform the functions of the above-described embodiment(s). For thispurpose, the program is provided to the computer for example via anetwork or from a recording medium of various types serving as thememory device (for example, computer-readable medium).

While the present invention has been described with reference toexemplary embodiments, it is to be understood that the invention is notlimited to the disclosed exemplary embodiments. The scope of thefollowing claims is to be accorded the broadest interpretation so as toencompass all such modifications and equivalent structures andfunctions.

This application claims the benefit of Japanese Patent Application No.2011-020089, filed Feb. 1, 2011, which is hereby incorporated byreference herein in its entirety.

1-13. (canceled)
 14. An image processing apparatus comprising: anacquisition unit configured to acquire a tomogram of a fundus of an eyeto be examined; a detection unit configured to detect a cribrosa laminafrom the tomogram acquired by said acquisition unit; and a calculationunit configured to calculate a thickness of the cribrosa lamina detectedby said detection unit.
 15. The apparatus according to claim 14, whereinsaid detection unit detects a predetermined layer boundary from thetomogram, and detects the cribrosa lamina based on the layer boundary.16. The apparatus according to claim 15, wherein the predetermined layerboundary is a retinal pigment epithelium boundary.
 17. The apparatusaccording to claim 14, further comprising a comparison unit configuredto compare the thickness of the cribrosa lamina calculated by saidcalculation unit with a reference value of the thickness of the cribrosalamina.
 18. The apparatus according to claim 15, further comprising acomparison unit configured to compare the thickness of the cribrosalamina calculated by said calculation unit with a reference value of thethickness of the cribrosa lamina.
 19. The apparatus according to claim16, further comprising a comparison unit configured to compare thethickness of the cribrosa lamina calculated by said calculation unitwith a reference value of the thickness of the cribrosa lamina.
 20. Theapparatus according to claim 17, further comprising a holding unitconfigured to hold the reference value.
 21. The apparatus according toclaim 18, further comprising a holding unit configured to hold thereference value.
 22. The apparatus according to claim 19, furthercomprising a holding unit configured to hold the reference value. 23.The apparatus according to claim 17, further comprising a specifyingunit configured to specify an anomaly portion of the eye to be examinedbased on a comparison result obtained by said comparison unit.
 24. Theapparatus according to claim 18, further comprising a specifying unitconfigured to specify an anomaly portion of the eye to be examined basedon a comparison result obtained by said comparison unit.
 25. Theapparatus according to claim 19, further comprising a specifying unitconfigured to specify an anomaly portion of the eye to be examined basedon a comparison result obtained by said comparison unit.
 26. Theapparatus according to claim 14, further comprising an informationacquisition unit configured to acquire information associated with avisual function of the eye to be examined based on the thickness of thecribrosa lamina.
 27. The apparatus according to claim 15, furthercomprising an information acquisition unit configured to acquireinformation associated with a visual function of the eye to be examinedbased on the thickness of the cribrosa lamina.
 28. The apparatusaccording to claim 26, wherein the information associated with thevisual function is information indicating a visual function drop riskdegree.
 29. The apparatus according to claim 14, wherein the tomogram isa two-dimensional tomogram including a depth direction of the fundus.30. An image processing method comprising: an acquisition step ofacquiring a tomogram of a fundus of an eye to be examined; a detectionstep of detecting a cribrosa lamina from the tomogram acquired in theacquisition step; and a calculation step of calculating a thickness ofthe cribrosa lamina detected in the detection step.
 31. The methodaccording to claim 30, wherein the detection step detects apredetermined layer boundary from the tomogram, and detects the cribrosalamina based on the layer boundary.
 32. The method according to claim31, wherein the predetermined layer boundary is a retinal pigmentepithelium boundary.
 33. A non-transitory computer-readable storagemedium storing a program for controlling a computer to executerespective steps of an image processing method, the method comprising:an acquisition step of acquiring a tomogram of a fundus of an eye to beexamined; a detection step of detecting a cribrosa lamina from thetomogram acquired in the acquisition step; and a calculation step ofcalculating a thickness of the cribrosa lamina detected in the detectionstep.